"Quit Talking and Learn English!": Conflicting Language Ideologies in an ESL Classroom
Bibliographic record
Abstract
This article addresses the relationship between educational theory—as manifested in particular ideologies of teaching and learning—and classroom practice. Based on an ethnographic study of English‐as‐a‐second‐language (ESL) learning at a Canadian senior public school, I outline a conflict between two language ideologies that give shape to, and are shaped by, the classroom practices of the ESL teacher, his assistants, and the students. I discuss the implications of this ideological conflict in terms of the opportunities ESL students are given, and that they create for themselves, to practice speaking English. I end by outlining how these findings can be used to shape educational policy as it relates to ESL classroom curricula in order to create a more equitable learning environment for ESL students.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.038 | 0.062 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".